Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "242" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 34 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 34 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460017 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 21.434145 | 0.518897 | -0.354613 | 0.981559 | 1.700943 | 0.656464 | -1.087302 | 0.322767 | 0.4087 | 0.5534 | 0.3538 | nan | nan |
| 2460016 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 20.917539 | 1.156334 | -0.077210 | 1.425097 | 1.355563 | 0.879535 | -1.491353 | -0.554712 | 0.4187 | 0.5574 | 0.3535 | nan | nan |
| 2460015 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 23.439705 | 0.694843 | -0.394169 | 1.038163 | 1.518939 | 0.669252 | -0.462805 | -0.255349 | 0.4295 | 0.5676 | 0.3516 | nan | nan |
| 2460014 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 27.176440 | 0.573913 | -0.420849 | 0.807837 | 2.099195 | -0.083137 | -0.470959 | 0.986585 | 0.3864 | 0.5518 | 0.3637 | nan | nan |
| 2460013 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 23.001228 | 0.587906 | -0.366062 | 1.149454 | 1.457290 | 0.759424 | -0.878736 | -0.519531 | 0.4223 | 0.5682 | 0.3601 | nan | nan |
| 2460012 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 21.943924 | 0.519570 | -0.499116 | 0.964577 | 1.591744 | 0.895135 | -0.501438 | -0.300317 | 0.4274 | 0.5710 | 0.3547 | nan | nan |
| 2460011 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 23.432230 | 0.783188 | -0.507970 | 1.433489 | 3.564755 | 2.923098 | -0.740613 | -0.509184 | 0.4395 | 0.5782 | 0.3633 | nan | nan |
| 2460010 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 26.390229 | 0.864543 | -0.010475 | 1.332659 | 2.971012 | 0.628283 | -0.788434 | -0.391097 | 0.4476 | 0.5945 | 0.3704 | nan | nan |
| 2460009 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 21.799420 | 1.233455 | 0.232615 | 1.559759 | 2.620658 | 1.932482 | -0.976834 | -0.899101 | 0.4532 | 0.5948 | 0.3725 | nan | nan |
| 2460008 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 26.180313 | 1.243026 | 0.308387 | 1.855514 | 2.476532 | 1.578935 | 0.441188 | 0.860965 | 0.4944 | 0.6277 | 0.3380 | nan | nan |
| 2460007 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 22.466329 | 0.259340 | -0.356817 | 0.918641 | 1.415566 | 0.620055 | -1.060617 | -0.265929 | 0.4575 | 0.6013 | 0.3592 | nan | nan |
| 2459999 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.4681 | 0.6219 | 0.3321 | nan | nan |
| 2459998 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 20.646187 | 0.423949 | -0.227385 | 0.985028 | 1.574666 | 0.830729 | -1.211406 | -0.051524 | 0.4454 | 0.6043 | 0.3837 | nan | nan |
| 2459997 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 20.529523 | 1.032781 | 0.397181 | 1.466185 | 2.611831 | 1.615181 | -1.282062 | -1.163361 | 0.4539 | 0.6137 | 0.3878 | nan | nan |
| 2459996 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 22.747140 | 0.753150 | -0.238064 | 1.121900 | 2.150279 | 0.879043 | 0.092358 | -0.155520 | 0.4672 | 0.6228 | 0.3945 | nan | nan |
| 2459995 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 24.607166 | 0.395887 | -0.216592 | 1.141894 | 2.335318 | 1.347076 | -0.669576 | -0.544292 | 0.4660 | 0.6196 | 0.3854 | nan | nan |
| 2459994 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 19.616193 | 0.237606 | 0.091797 | 1.011892 | 4.844461 | 1.100857 | 12.647280 | -0.386418 | 0.4951 | 0.6139 | 0.3855 | nan | nan |
| 2459993 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 21.118548 | 1.851494 | 1.021774 | 1.671781 | 4.275021 | 1.814520 | 3.164564 | -0.246041 | 0.4671 | 0.6071 | 0.4029 | nan | nan |
| 2459991 | RF_ok | 100.00% | 99.95% | 99.89% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | 0.3772 | 0.3415 | 0.2427 | nan | nan |
| 2459990 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 23.372127 | 1.213299 | 0.484662 | 1.805536 | 2.589354 | 1.921944 | -1.403997 | -0.609386 | 0.4431 | 0.6061 | 0.3887 | nan | nan |
| 2459989 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 17.368475 | 0.454986 | 0.694770 | 1.211627 | 2.417538 | 0.474771 | -0.915439 | -0.265434 | 0.5105 | 0.6111 | 0.3961 | nan | nan |
| 2459988 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 22.156996 | 1.619309 | 0.895368 | 1.890300 | 4.599608 | 2.213428 | 3.317864 | -0.218517 | 0.4859 | 0.6102 | 0.3856 | nan | nan |
| 2459987 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 22.368433 | 0.772192 | 0.383166 | 1.423683 | 1.803183 | 1.149411 | -1.311912 | -0.135201 | 0.4524 | 0.6111 | 0.3802 | nan | nan |
| 2459986 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 29.196751 | 0.522043 | -0.074174 | 1.322161 | 2.823097 | 1.496255 | 0.729708 | 0.977175 | 0.4816 | 0.6386 | 0.3425 | nan | nan |
| 2459985 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 18.423803 | 0.506891 | 0.253598 | 0.981403 | 2.624849 | 0.547721 | 4.515534 | -0.319019 | 0.5108 | 0.6177 | 0.3908 | nan | nan |
| 2459984 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 23.951829 | 0.105990 | -0.135022 | 0.997401 | 3.087513 | 0.284456 | -0.736498 | -0.829674 | 0.4796 | 0.6334 | 0.3645 | nan | nan |
| 2459983 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 24.400782 | 0.862677 | 0.375485 | 1.688789 | 3.191917 | 1.929649 | -0.201202 | 0.236528 | 0.4753 | 0.6402 | 0.3407 | nan | nan |
| 2459982 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 15.978780 | -0.553180 | -0.116567 | 0.934239 | 0.246048 | -0.296970 | -0.105268 | -0.169942 | 0.5656 | 0.6794 | 0.2761 | nan | nan |
| 2459981 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 23.156257 | 0.847382 | 0.560897 | 2.067266 | 3.595873 | 2.333881 | -0.135869 | -0.469175 | 0.4530 | 0.6185 | 0.3813 | nan | nan |
| 2459980 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 21.342033 | 0.316368 | 0.238408 | 1.315050 | 2.982531 | 1.471970 | 0.722935 | 0.653036 | 0.5164 | 0.6554 | 0.3056 | nan | nan |
| 2459979 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 23.861946 | 0.529511 | 0.268733 | 1.365561 | 2.581894 | 0.948295 | -0.448152 | -0.096411 | 0.4511 | 0.6142 | 0.3815 | nan | nan |
| 2459978 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 24.048094 | 0.720723 | 0.430045 | 1.671903 | 2.701431 | 1.736991 | -1.259098 | 0.160794 | 0.4469 | 0.6130 | 0.3878 | nan | nan |
| 2459977 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 22.432267 | 0.851364 | 0.171830 | 1.292149 | 3.613748 | 1.510302 | -1.113915 | -0.951190 | 0.4091 | 0.5723 | 0.3479 | nan | nan |
| 2459976 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 25.097624 | -0.077915 | -0.210511 | 1.109144 | 2.314188 | 1.347272 | -0.690827 | 0.389133 | 0.4597 | 0.6219 | 0.3764 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 21.434145 | 0.518897 | 21.434145 | 0.981559 | -0.354613 | 0.656464 | 1.700943 | 0.322767 | -1.087302 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 20.917539 | 1.156334 | 20.917539 | 1.425097 | -0.077210 | 0.879535 | 1.355563 | -0.554712 | -1.491353 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 23.439705 | 0.694843 | 23.439705 | 1.038163 | -0.394169 | 0.669252 | 1.518939 | -0.255349 | -0.462805 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 27.176440 | 27.176440 | 0.573913 | -0.420849 | 0.807837 | 2.099195 | -0.083137 | -0.470959 | 0.986585 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 23.001228 | 23.001228 | 0.587906 | -0.366062 | 1.149454 | 1.457290 | 0.759424 | -0.878736 | -0.519531 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 21.943924 | 21.943924 | 0.519570 | -0.499116 | 0.964577 | 1.591744 | 0.895135 | -0.501438 | -0.300317 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 23.432230 | 23.432230 | 0.783188 | -0.507970 | 1.433489 | 3.564755 | 2.923098 | -0.740613 | -0.509184 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 26.390229 | 26.390229 | 0.864543 | -0.010475 | 1.332659 | 2.971012 | 0.628283 | -0.788434 | -0.391097 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 21.799420 | 21.799420 | 1.233455 | 0.232615 | 1.559759 | 2.620658 | 1.932482 | -0.976834 | -0.899101 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 26.180313 | 1.243026 | 26.180313 | 1.855514 | 0.308387 | 1.578935 | 2.476532 | 0.860965 | 0.441188 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 22.466329 | 22.466329 | 0.259340 | -0.356817 | 0.918641 | 1.415566 | 0.620055 | -1.060617 | -0.265929 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 20.646187 | 20.646187 | 0.423949 | -0.227385 | 0.985028 | 1.574666 | 0.830729 | -1.211406 | -0.051524 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 20.529523 | 20.529523 | 1.032781 | 0.397181 | 1.466185 | 2.611831 | 1.615181 | -1.282062 | -1.163361 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 22.747140 | 22.747140 | 0.753150 | -0.238064 | 1.121900 | 2.150279 | 0.879043 | 0.092358 | -0.155520 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 24.607166 | 24.607166 | 0.395887 | -0.216592 | 1.141894 | 2.335318 | 1.347076 | -0.669576 | -0.544292 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 19.616193 | 19.616193 | 0.237606 | 0.091797 | 1.011892 | 4.844461 | 1.100857 | 12.647280 | -0.386418 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 21.118548 | 21.118548 | 1.851494 | 1.021774 | 1.671781 | 4.275021 | 1.814520 | 3.164564 | -0.246041 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 23.372127 | 1.213299 | 23.372127 | 1.805536 | 0.484662 | 1.921944 | 2.589354 | -0.609386 | -1.403997 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 17.368475 | 0.454986 | 17.368475 | 1.211627 | 0.694770 | 0.474771 | 2.417538 | -0.265434 | -0.915439 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 22.156996 | 1.619309 | 22.156996 | 1.890300 | 0.895368 | 2.213428 | 4.599608 | -0.218517 | 3.317864 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 22.368433 | 22.368433 | 0.772192 | 0.383166 | 1.423683 | 1.803183 | 1.149411 | -1.311912 | -0.135201 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 29.196751 | 0.522043 | 29.196751 | 1.322161 | -0.074174 | 1.496255 | 2.823097 | 0.977175 | 0.729708 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 18.423803 | 0.506891 | 18.423803 | 0.981403 | 0.253598 | 0.547721 | 2.624849 | -0.319019 | 4.515534 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 23.951829 | 23.951829 | 0.105990 | -0.135022 | 0.997401 | 3.087513 | 0.284456 | -0.736498 | -0.829674 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 24.400782 | 24.400782 | 0.862677 | 0.375485 | 1.688789 | 3.191917 | 1.929649 | -0.201202 | 0.236528 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 15.978780 | 15.978780 | -0.553180 | -0.116567 | 0.934239 | 0.246048 | -0.296970 | -0.105268 | -0.169942 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 23.156257 | 0.847382 | 23.156257 | 2.067266 | 0.560897 | 2.333881 | 3.595873 | -0.469175 | -0.135869 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 21.342033 | 0.316368 | 21.342033 | 1.315050 | 0.238408 | 1.471970 | 2.982531 | 0.653036 | 0.722935 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 23.861946 | 23.861946 | 0.529511 | 0.268733 | 1.365561 | 2.581894 | 0.948295 | -0.448152 | -0.096411 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 24.048094 | 0.720723 | 24.048094 | 1.671903 | 0.430045 | 1.736991 | 2.701431 | 0.160794 | -1.259098 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 22.432267 | 22.432267 | 0.851364 | 0.171830 | 1.292149 | 3.613748 | 1.510302 | -1.113915 | -0.951190 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 242 | N19 | RF_ok | ee Shape | 25.097624 | -0.077915 | 25.097624 | 1.109144 | -0.210511 | 1.347272 | 2.314188 | 0.389133 | -0.690827 |